On a global scale, a considerable amount of life, property, and resources are lost because of the increasing frequency and severity of flooding occurrences. This necessitates the development of worldwide, thorough flood risk assessments and urbanization policies. This work used sophisticated remote sensing data hydraulic models to create an effective and appropriate methodology to flood risk assessment in a coastal city located in in western Algeria. In the first stage, Sentinel-2 optical data was classified using deep learning and machine learning methods for land cover and land use (LULC). The CNN deep model based on LULC was selected because of its outstanding overall accuracy. Then, a 1-D HEC-RAS hydraulic model was performed, integrating LULC maps with a higher precision, topography using a digital surface model (DSM) derived from Pléiades tri-stereo data, and another digital elevation model (12 meters). Flood hydrographs were as well constructed for four scenarios (10, 20, 50, and 100 years) using hydrometric data. The 1D flood mode was indeed validated using a flood event data. Flood hazard, LULC and flood risk maps were derived. Results show the high flood hazard areas are concentrated on the left bank of the Oued Allala river and urban cities near to the coast. According to the results of the flood hazard simulation of 100 years, built-up areas and roads are the LULC classes most affected by flood hazard, with more than 94.4% and 69.34 % for DSM tri-stereo and DEM models, respectively. As well, results of flood risk assessment by combining hazard risk and LULC vulnerability show that for the DSM model, 0.48%, 44.55%, and 53.11%, and 54.04% of flooded areas are in low, medium, and high flood risk, respectively.

For the DEM model, 3.14%, 45%, and 51.04% of flooded areas are in low, medium, and high flood risk, respectively. Results confirmed that topographic resolution of models and LULC accuracy of CNN models can highly affect hydraulic simulation output results. Based on the obtained results, Ténès city needs necessary planning for flood risk management, particularly in the coastal area. Derived maps can serve as valuable information for regional and national decision-making.